In CNS neuroscience & therapeutics
AIMS : Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS).
METHODS : We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis.
RESULTS : In the feature evaluation of SVO-AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis. In SVO-AIS, SVD performed better than regular clinical data, which is the opposite of LAA-AIS. Among all models, eXtreme gradient boosting (XGBoost) method with optimal index (OI) has the best performance to predict excellent outcome in SVO-AIS. [0.91 (0.84-0.97)].
CONCLUSIONS : Our results revealed that different SVD markers had distinct prognostic weights in AIS patients, and SVD burden alone may accurately predict the SVO-AIS patients' prognosis.
Wang Xueyang, Lyu Jinhao, Meng Zhihua, Wu Xiaoyan, Chen Wen, Wang Guohua, Niu Qingliang, Li Xin, Bian Yitong, Han Dan, Guo Weiting, Yang Shuai, Bian Xiangbing, Lan Yina, Wang Liuxian, Duan Qi, Zhang Tingyang, Duan Caohui, Tian Chenglin, Chen Ling, Lou Xin
2023-Jan-17
acute ischemic stroke, cerebral small vessel disease, machine learning, prediction model
